Overview

Dataset statistics

Number of variables11
Number of observations5481258
Missing cells497105
Missing cells (%)0.8%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory460.0 MiB
Average record size in memory88.0 B

Variable types

Categorical5
Numeric6

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
filename has a high cardinality: 2216 distinct values High cardinality
sha256 has a high cardinality: 840216 distinct values High cardinality
imp_hash has a high cardinality: 159784 distinct values High cardinality
sec_md5 has a high cardinality: 1607067 distinct values High cardinality
sec_name has a high cardinality: 27944 distinct values High cardinality
sec_chi2 is highly correlated with raw_sizeHigh correlation
sec_entropy is highly correlated with raw_size and 1 other fieldsHigh correlation
raw_size is highly correlated with sec_chi2 and 2 other fieldsHigh correlation
virtual_size is highly correlated with sec_entropy and 1 other fieldsHigh correlation
sec_chi2 is highly correlated with raw_sizeHigh correlation
sec_entropy is highly correlated with raw_sizeHigh correlation
raw_size is highly correlated with sec_chi2 and 2 other fieldsHigh correlation
virtual_size is highly correlated with raw_sizeHigh correlation
raw_size is highly correlated with virtual_size and 1 other fieldsHigh correlation
virtual_size is highly correlated with raw_sizeHigh correlation
virtual_address is highly correlated with raw_sizeHigh correlation
imp_hash has 479933 (8.8%) missing values Missing
sec_chi2 is highly skewed (γ1 = 191.4928628) Skewed
raw_size is highly skewed (γ1 = 278.2295116) Skewed
virtual_size is highly skewed (γ1 = 153.692833) Skewed
virtual_address is highly skewed (γ1 = 99.64981013) Skewed
sec_entropy has 859600 (15.7%) zeros Zeros
raw_size has 569504 (10.4%) zeros Zeros

Reproduction

Analysis started2022-08-01 04:58:55.940507
Analysis finished2022-08-01 05:01:03.533664
Duration2 minutes and 7.59 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

filename
Categorical

HIGH CARDINALITY

Distinct2216
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.8 MiB
2022042300/2022042300_51
 
11810
2022042300/2022042300_58
 
11569
2022042301/2022042301_0
 
11532
2022042300/2022042300_59
 
11252
2022042400/2022042400_59
 
11239
Other values (2211)
5423856 

Length

Max length24
Median length24
Mean length23.83266396
Min length23

Characters and Unicode

Total characters130632980
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022042215/2022042215_40
2nd row2022042215/2022042215_40
3rd row2022042215/2022042215_40
4th row2022042215/2022042215_40
5th row2022042215/2022042215_40

Common Values

ValueCountFrequency (%)
2022042300/2022042300_5111810
 
0.2%
2022042300/2022042300_5811569
 
0.2%
2022042301/2022042301_011532
 
0.2%
2022042300/2022042300_5911252
 
0.2%
2022042400/2022042400_5911239
 
0.2%
2022042300/2022042300_4811073
 
0.2%
2022042300/2022042300_5310753
 
0.2%
2022042301/2022042301_110718
 
0.2%
2022042401/2022042401_010462
 
0.2%
2022042400/2022042400_5110441
 
0.2%
Other values (2206)5370409
98.0%

Length

2022-08-01T15:01:03.651586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022042300/2022042300_5111810
 
0.2%
2022042300/2022042300_5811569
 
0.2%
2022042301/2022042301_011532
 
0.2%
2022042300/2022042300_5911252
 
0.2%
2022042400/2022042400_5911239
 
0.2%
2022042300/2022042300_4811073
 
0.2%
2022042300/2022042300_5310753
 
0.2%
2022042301/2022042301_110718
 
0.2%
2022042401/2022042401_010462
 
0.2%
2022042400/2022042400_5110441
 
0.2%
Other values (2206)5370409
98.0%

Most occurring characters

ValueCountFrequency (%)
250384859
38.6%
029445116
22.5%
414470521
 
11.1%
39959925
 
7.6%
16865350
 
5.3%
/5481258
 
4.2%
_5481258
 
4.2%
52391795
 
1.8%
61628381
 
1.2%
71618337
 
1.2%
Other values (2)2906180
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number119670464
91.6%
Other Punctuation5481258
 
4.2%
Connector Punctuation5481258
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
250384859
42.1%
029445116
24.6%
414470521
 
12.1%
39959925
 
8.3%
16865350
 
5.7%
52391795
 
2.0%
61628381
 
1.4%
71618337
 
1.4%
81547064
 
1.3%
91359116
 
1.1%
Other Punctuation
ValueCountFrequency (%)
/5481258
100.0%
Connector Punctuation
ValueCountFrequency (%)
_5481258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common130632980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
250384859
38.6%
029445116
22.5%
414470521
 
11.1%
39959925
 
7.6%
16865350
 
5.3%
/5481258
 
4.2%
_5481258
 
4.2%
52391795
 
1.8%
61628381
 
1.2%
71618337
 
1.2%
Other values (2)2906180
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII130632980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
250384859
38.6%
029445116
22.5%
414470521
 
11.1%
39959925
 
7.6%
16865350
 
5.3%
/5481258
 
4.2%
_5481258
 
4.2%
52391795
 
1.8%
61628381
 
1.2%
71618337
 
1.2%
Other values (2)2906180
 
2.2%

win_count
Real number (ℝ≥0)

Distinct371743
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120696.1499
Minimum1
Maximum374945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 MiB
2022-08-01T15:01:03.789698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7314
Q143079
median96158
Q3181252
95-th percentile320307
Maximum374945
Range374944
Interquartile range (IQR)138173

Descriptive statistics

Standard deviation95894.56973
Coefficient of variation (CV)0.794512251
Kurtosis-0.2029030296
Mean120696.1499
Median Absolute Deviation (MAD)61828
Skewness0.8458802077
Sum6.615667371 × 1011
Variance9195768504
MonotonicityNot monotonic
2022-08-01T15:01:03.923689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14094119
 
< 0.1%
306699
 
< 0.1%
2528994
 
< 0.1%
2998994
 
< 0.1%
55993
 
< 0.1%
875292
 
< 0.1%
258092
 
< 0.1%
2945791
 
< 0.1%
646190
 
< 0.1%
750490
 
< 0.1%
Other values (371733)5480304
> 99.9%
ValueCountFrequency (%)
135
< 0.1%
231
< 0.1%
349
< 0.1%
434
< 0.1%
534
< 0.1%
630
< 0.1%
726
< 0.1%
829
< 0.1%
931
< 0.1%
1030
< 0.1%
ValueCountFrequency (%)
3749457
< 0.1%
3749444
< 0.1%
3749433
< 0.1%
3749421
 
< 0.1%
3749416
< 0.1%
3749403
< 0.1%
3749394
< 0.1%
3749381
 
< 0.1%
3749373
< 0.1%
3749363
< 0.1%

sha256
Categorical

HIGH CARDINALITY

Distinct840216
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size41.8 MiB
4ccfc297572d79c8e64aaef9855e8a879c19cbcecdc0050218bf6ce656f5710b
 
792
77980723f53e66234368e2db43fda4e640fcfae134dfdd57c62fb50fd53b2273
 
696
74e0c5d03137d87fcb57f8bb3f2e16e6a540ee02acf11f9f38c688d4ce9ee65c
 
664
6d3fcefcf9130c19b1ae9538e8870c70c68903a20e7650a3038123bea0df7997
 
649
2aba55077ca3974c001f02252f014022620487555be964e615078711a6022e36
 
592
Other values (840211)
5477865 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters350800512
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8364 ?
Unique (%)0.2%

Sample

1st rowb6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6fa
2nd rowb6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6fa
3rd rowb6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6fa
4th rowb6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6fa
5th rowb6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6fa

Common Values

ValueCountFrequency (%)
4ccfc297572d79c8e64aaef9855e8a879c19cbcecdc0050218bf6ce656f5710b792
 
< 0.1%
77980723f53e66234368e2db43fda4e640fcfae134dfdd57c62fb50fd53b2273696
 
< 0.1%
74e0c5d03137d87fcb57f8bb3f2e16e6a540ee02acf11f9f38c688d4ce9ee65c664
 
< 0.1%
6d3fcefcf9130c19b1ae9538e8870c70c68903a20e7650a3038123bea0df7997649
 
< 0.1%
2aba55077ca3974c001f02252f014022620487555be964e615078711a6022e36592
 
< 0.1%
8f6cc96686e671bc8f2d980f39ffe125517c4ce2407755289e52b19cdaee9961584
 
< 0.1%
9d5b4887dd3166f6284b3220de0c77136f3dc795f4d7e40711472f9f24b390f4576
 
< 0.1%
65ef2d9df6f831af1c06336cc9ce4b9cf77deb59dae36ea7ed1016a43702a8bb576
 
< 0.1%
e69356111240657e6435edf2e3a4bbac9c89957ef2d34fc620b8b7dbf564a862560
 
< 0.1%
f97aef6102ec86f06c1b143a8bea30a250624b5fe243f97c07fda439cb7a21c4552
 
< 0.1%
Other values (840206)5475017
99.9%

Length

2022-08-01T15:01:04.068977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4ccfc297572d79c8e64aaef9855e8a879c19cbcecdc0050218bf6ce656f5710b792
 
< 0.1%
77980723f53e66234368e2db43fda4e640fcfae134dfdd57c62fb50fd53b2273696
 
< 0.1%
74e0c5d03137d87fcb57f8bb3f2e16e6a540ee02acf11f9f38c688d4ce9ee65c664
 
< 0.1%
6d3fcefcf9130c19b1ae9538e8870c70c68903a20e7650a3038123bea0df7997649
 
< 0.1%
2aba55077ca3974c001f02252f014022620487555be964e615078711a6022e36592
 
< 0.1%
8f6cc96686e671bc8f2d980f39ffe125517c4ce2407755289e52b19cdaee9961584
 
< 0.1%
9d5b4887dd3166f6284b3220de0c77136f3dc795f4d7e40711472f9f24b390f4576
 
< 0.1%
65ef2d9df6f831af1c06336cc9ce4b9cf77deb59dae36ea7ed1016a43702a8bb576
 
< 0.1%
e69356111240657e6435edf2e3a4bbac9c89957ef2d34fc620b8b7dbf564a862560
 
< 0.1%
f97aef6102ec86f06c1b143a8bea30a250624b5fe243f97c07fda439cb7a21c4552
 
< 0.1%
Other values (840206)5475017
99.9%

Most occurring characters

ValueCountFrequency (%)
621988869
 
6.3%
021963668
 
6.3%
221950839
 
6.3%
921948507
 
6.3%
e21941746
 
6.3%
a21938702
 
6.3%
121936695
 
6.3%
c21924943
 
6.2%
b21918784
 
6.2%
521916947
 
6.2%
Other values (6)131370812
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number219300502
62.5%
Lowercase Letter131500010
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
621988869
10.0%
021963668
10.0%
221950839
10.0%
921948507
10.0%
121936695
10.0%
521916947
10.0%
421904960
10.0%
321900530
10.0%
721898722
10.0%
821890765
10.0%
Lowercase Letter
ValueCountFrequency (%)
e21941746
16.7%
a21938702
16.7%
c21924943
16.7%
b21918784
16.7%
f21909798
16.7%
d21866037
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common219300502
62.5%
Latin131500010
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
621988869
10.0%
021963668
10.0%
221950839
10.0%
921948507
10.0%
121936695
10.0%
521916947
10.0%
421904960
10.0%
321900530
10.0%
721898722
10.0%
821890765
10.0%
Latin
ValueCountFrequency (%)
e21941746
16.7%
a21938702
16.7%
c21924943
16.7%
b21918784
16.7%
f21909798
16.7%
d21866037
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII350800512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
621988869
 
6.3%
021963668
 
6.3%
221950839
 
6.3%
921948507
 
6.3%
e21941746
 
6.3%
a21938702
 
6.3%
121936695
 
6.3%
c21924943
 
6.2%
b21918784
 
6.2%
521916947
 
6.2%
Other values (6)131370812
37.4%

imp_hash
Categorical

HIGH CARDINALITY
MISSING

Distinct159784
Distinct (%)3.2%
Missing479933
Missing (%)8.8%
Memory size41.8 MiB
431cb9bbc479c64cb0d873043f4de547
 
258069
d66b543d0999c7628a55690ef9b1c96e
 
158730
73effd46557538d5fa5561eee3ffc59c
 
141030
835a0f00bf1f2c5420f77cabc26e254c
 
139032
9dc46f318397655dea2844d0fd08e2ab
 
127552
Other values (159779)
4176912 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters160042400
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)< 0.1%

Sample

1st rowa9192bab5c7c795c7488b69a1853f9c2
2nd rowa9192bab5c7c795c7488b69a1853f9c2
3rd rowa9192bab5c7c795c7488b69a1853f9c2
4th rowa9192bab5c7c795c7488b69a1853f9c2
5th rowa9192bab5c7c795c7488b69a1853f9c2

Common Values

ValueCountFrequency (%)
431cb9bbc479c64cb0d873043f4de547258069
 
4.7%
d66b543d0999c7628a55690ef9b1c96e158730
 
2.9%
73effd46557538d5fa5561eee3ffc59c141030
 
2.6%
835a0f00bf1f2c5420f77cabc26e254c139032
 
2.5%
9dc46f318397655dea2844d0fd08e2ab127552
 
2.3%
dae02f32a21e03ce65412f6e56942daa127404
 
2.3%
359d89624a26d1e756c3e9d6782d6eb0102135
 
1.9%
f34d5f2d4577ed6d9ceec516c1f5a74488854
 
1.6%
3a2003ea545fe942681da9e7683ebb5886354
 
1.6%
25c7ac00c91884fd2923a489ae9dfbca74647
 
1.4%
Other values (159774)3697518
67.5%
(Missing)479933
 
8.8%

Length

2022-08-01T15:01:04.185229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
431cb9bbc479c64cb0d873043f4de547258069
 
5.2%
d66b543d0999c7628a55690ef9b1c96e158730
 
3.2%
73effd46557538d5fa5561eee3ffc59c141030
 
2.8%
835a0f00bf1f2c5420f77cabc26e254c139032
 
2.8%
9dc46f318397655dea2844d0fd08e2ab127552
 
2.6%
dae02f32a21e03ce65412f6e56942daa127404
 
2.5%
359d89624a26d1e756c3e9d6782d6eb0102135
 
2.0%
f34d5f2d4577ed6d9ceec516c1f5a74488854
 
1.8%
3a2003ea545fe942681da9e7683ebb5886354
 
1.7%
25c7ac00c91884fd2923a489ae9dfbca74647
 
1.5%
Other values (159774)3697518
73.9%

Most occurring characters

ValueCountFrequency (%)
511182166
 
7.0%
c10822911
 
6.8%
410743513
 
6.7%
e10612921
 
6.6%
610498432
 
6.6%
910359316
 
6.5%
310178322
 
6.4%
f10054384
 
6.3%
d9888142
 
6.2%
29848086
 
6.2%
Other values (6)55854207
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99879359
62.4%
Lowercase Letter60163041
37.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
511182166
11.2%
410743513
10.8%
610498432
10.5%
910359316
10.4%
310178322
10.2%
29848086
9.9%
79454541
9.5%
09446515
9.5%
89437353
9.4%
18731115
8.7%
Lowercase Letter
ValueCountFrequency (%)
c10822911
18.0%
e10612921
17.6%
f10054384
16.7%
d9888142
16.4%
a9396199
15.6%
b9388484
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common99879359
62.4%
Latin60163041
37.6%

Most frequent character per script

Common
ValueCountFrequency (%)
511182166
11.2%
410743513
10.8%
610498432
10.5%
910359316
10.4%
310178322
10.2%
29848086
9.9%
79454541
9.5%
09446515
9.5%
89437353
9.4%
18731115
8.7%
Latin
ValueCountFrequency (%)
c10822911
18.0%
e10612921
17.6%
f10054384
16.7%
d9888142
16.4%
a9396199
15.6%
b9388484
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII160042400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
511182166
 
7.0%
c10822911
 
6.8%
410743513
 
6.7%
e10612921
 
6.6%
610498432
 
6.6%
910359316
 
6.5%
310178322
 
6.4%
f10054384
 
6.3%
d9888142
 
6.2%
29848086
 
6.2%
Other values (6)55854207
34.9%

sec_chi2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1345304
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3933122.516
Minimum-1
Maximum6.134842163 × 1010
Zeros3
Zeros (%)< 0.1%
Negative698988
Negative (%)12.8%
Memory size41.8 MiB
2022-08-01T15:01:04.312601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q152818
median141183.64
Q3927711.38
95-th percentile7920753.075
Maximum6.134842163 × 1010
Range6.134842163 × 1010
Interquartile range (IQR)874893.38

Descriptive statistics

Standard deviation70664906.69
Coefficient of variation (CV)17.96661721
Kurtosis114696.2164
Mean3933122.516
Median Absolute Deviation (MAD)141184.64
Skewness191.4928628
Sum2.155845926 × 1013
Variance4.993529038 × 1015
MonotonicityNot monotonic
2022-08-01T15:01:04.448848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1698988
 
12.8%
104448075184
 
1.4%
12500140106
 
0.7%
12852236393
 
0.7%
11159133945
 
0.6%
13056033849
 
0.6%
33380.531743
 
0.6%
151838.2531742
 
0.6%
142937.3631742
 
0.6%
13004931294
 
0.6%
Other values (1345294)4436272
80.9%
ValueCountFrequency (%)
-1698988
12.8%
03
 
< 0.1%
0.951
 
< 0.1%
81
 
< 0.1%
13.51
 
< 0.1%
33.751
 
< 0.1%
50.942
 
< 0.1%
57.24
 
< 0.1%
59.671
 
< 0.1%
601
 
< 0.1%
ValueCountFrequency (%)
6.134842163 × 10101
< 0.1%
2.675428557 × 10101
< 0.1%
2.067147776 × 10101
< 0.1%
1.50892032 × 10101
< 0.1%
1.282384282 × 10101
< 0.1%
1.240008909 × 10101
< 0.1%
1.201863782 × 10102
< 0.1%
1.104028365 × 10101
< 0.1%
1.058207437 × 10101
< 0.1%
93614981122
< 0.1%

sec_entropy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct801
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.760878384
Minimum0
Maximum8
Zeros859600
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size41.8 MiB
2022-08-01T15:01:04.582320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.95
median4.32
Q35.87
95-th percentile7.62
Maximum8
Range8
Interquartile range (IQR)4.92

Descriptive statistics

Standard deviation2.53697836
Coefficient of variation (CV)0.674570699
Kurtosis-1.220241926
Mean3.760878384
Median Absolute Deviation (MAD)1.93
Skewness-0.253133845
Sum20614344.73
Variance6.436259202
MonotonicityNot monotonic
2022-08-01T15:01:04.718833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0859600
 
15.7%
0.269194
 
1.3%
852243
 
1.0%
0.0839352
 
0.7%
4.5839226
 
0.7%
5.2238894
 
0.7%
5.6537338
 
0.7%
0.5635451
 
0.6%
6.6235147
 
0.6%
3.6534704
 
0.6%
Other values (791)4240109
77.4%
ValueCountFrequency (%)
0859600
15.7%
0.015741
 
0.1%
0.0233634
 
0.6%
0.031892
 
< 0.1%
0.041930
 
< 0.1%
0.051836
 
< 0.1%
0.069474
 
0.2%
0.072014
 
< 0.1%
0.0839352
 
0.7%
0.091128
 
< 0.1%
ValueCountFrequency (%)
852243
1.0%
7.9924913
0.5%
7.9815267
 
0.3%
7.9710532
 
0.2%
7.967516
 
0.1%
7.9510940
 
0.2%
7.947098
 
0.1%
7.939574
 
0.2%
7.925437
 
0.1%
7.9122129
0.4%

sec_md5
Categorical

HIGH CARDINALITY

Distinct1607067
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size41.8 MiB
d41d8cd98f00b204e9800998ecf8427e
698988 
620f0b67a91f7f74151bc5be745b7110
 
75168
ee2380aae73db3186a5a54ce2f9ac7fc
 
33945
bf619eac0cdf3f68d496ea9344137e8b
 
33784
f8b18472fbf312f8a4756cc1aec8336c
 
31742
Other values (1607062)
4607631 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters175400256
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1337270 ?
Unique (%)24.4%

Sample

1st rowd04e1b35c9928ce904ea04494e9addd3
2nd rowee2380aae73db3186a5a54ce2f9ac7fc
3rd row3f6ce759ed044ca6fd40934ca8a1f973
4th rowd41d8cd98f00b204e9800998ecf8427e
5th rowcc77684f8c03f3a87d249f96bcd88fd5

Common Values

ValueCountFrequency (%)
d41d8cd98f00b204e9800998ecf8427e698988
 
12.8%
620f0b67a91f7f74151bc5be745b711075168
 
1.4%
ee2380aae73db3186a5a54ce2f9ac7fc33945
 
0.6%
bf619eac0cdf3f68d496ea9344137e8b33784
 
0.6%
f8b18472fbf312f8a4756cc1aec8336c31742
 
0.6%
c4d37e3f619b00a6c421902c34e1a3e331742
 
0.6%
9cba2455f83b88a48007a4003e3f3cbd31742
 
0.6%
1f354d76203061bfdd5a53dae48d543524501
 
0.4%
89ce79b3a1e62aeb2ea80a5018651a4422732
 
0.4%
7e016ed8299b52ab729134bb6f3806a020775
 
0.4%
Other values (1607057)4476139
81.7%

Length

2022-08-01T15:01:04.886507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d41d8cd98f00b204e9800998ecf8427e698988
 
12.8%
620f0b67a91f7f74151bc5be745b711075168
 
1.4%
ee2380aae73db3186a5a54ce2f9ac7fc33945
 
0.6%
bf619eac0cdf3f68d496ea9344137e8b33784
 
0.6%
f8b18472fbf312f8a4756cc1aec8336c31742
 
0.6%
c4d37e3f619b00a6c421902c34e1a3e331742
 
0.6%
9cba2455f83b88a48007a4003e3f3cbd31742
 
0.6%
1f354d76203061bfdd5a53dae48d543524501
 
0.4%
89ce79b3a1e62aeb2ea80a5018651a4422732
 
0.4%
7e016ed8299b52ab729134bb6f3806a020775
 
0.4%
Other values (1607057)4476139
81.7%

Most occurring characters

ValueCountFrequency (%)
813074569
 
7.5%
013064020
 
7.4%
912384418
 
7.1%
411916826
 
6.8%
e11851466
 
6.8%
d11403535
 
6.5%
f11115780
 
6.3%
c10828554
 
6.2%
210720618
 
6.1%
b10419080
 
5.9%
Other values (6)58621390
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number109950538
62.7%
Lowercase Letter65449718
37.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
813074569
11.9%
013064020
11.9%
912384418
11.3%
411916826
10.8%
210720618
9.8%
710294432
9.4%
110241838
9.3%
39634637
8.8%
69356729
8.5%
59262451
8.4%
Lowercase Letter
ValueCountFrequency (%)
e11851466
18.1%
d11403535
17.4%
f11115780
17.0%
c10828554
16.5%
b10419080
15.9%
a9831303
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common109950538
62.7%
Latin65449718
37.3%

Most frequent character per script

Common
ValueCountFrequency (%)
813074569
11.9%
013064020
11.9%
912384418
11.3%
411916826
10.8%
210720618
9.8%
710294432
9.4%
110241838
9.3%
39634637
8.8%
69356729
8.5%
59262451
8.4%
Latin
ValueCountFrequency (%)
e11851466
18.1%
d11403535
17.4%
f11115780
17.0%
c10828554
16.5%
b10419080
15.9%
a9831303
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII175400256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
813074569
 
7.5%
013064020
 
7.4%
912384418
 
7.1%
411916826
 
6.8%
e11851466
 
6.8%
d11403535
 
6.5%
f11115780
 
6.3%
c10828554
 
6.2%
210720618
 
6.1%
b10419080
 
5.9%
Other values (6)58621390
33.4%

raw_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct27645
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284140.9943
Minimum0
Maximum4294963712
Zeros569504
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size41.8 MiB
2022-08-01T15:01:05.013705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11024
median4608
Q341472
95-th percentile717312
Maximum4294963712
Range4294963712
Interquartile range (IQR)40448

Descriptive statistics

Standard deviation5924477.627
Coefficient of variation (CV)20.8504853
Kurtosis147144.4046
Mean284140.9943
Median Absolute Deviation (MAD)4608
Skewness278.2295116
Sum1.557450098 × 1012
Variance3.509943515 × 1013
MonotonicityNot monotonic
2022-08-01T15:01:05.145657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
512775838
 
14.2%
0569504
 
10.4%
4096414511
 
7.6%
1024273345
 
5.0%
2048197136
 
3.6%
1536186180
 
3.4%
12288137699
 
2.5%
2560126524
 
2.3%
819288934
 
1.6%
563283906
 
1.5%
Other values (27635)2627681
47.9%
ValueCountFrequency (%)
0569504
10.4%
14
 
< 0.1%
21
 
< 0.1%
46
 
< 0.1%
528
 
< 0.1%
62
 
< 0.1%
77
 
< 0.1%
839
 
< 0.1%
911
 
< 0.1%
104
 
< 0.1%
ValueCountFrequency (%)
42949637121
 
< 0.1%
42949596161
 
< 0.1%
37580989441
 
< 0.1%
19364874701
 
< 0.1%
116024988649
< 0.1%
9882951681
 
< 0.1%
8083962881
 
< 0.1%
7140264961
 
< 0.1%
7139993601
 
< 0.1%
7139758081
 
< 0.1%

virtual_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct310610
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359622.5707
Minimum0
Maximum4278277684
Zeros3514
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size41.8 MiB
2022-08-01T15:01:05.277905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q11540
median8340
Q361440
95-th percentile929792
Maximum4278277684
Range4278277684
Interquartile range (IQR)59900

Descriptive statistics

Standard deviation5686588.676
Coefficient of variation (CV)15.81265788
Kurtosis69840.8417
Mean359622.5707
Median Absolute Deviation (MAD)8256
Skewness153.692833
Sum1.971184093 × 1012
Variance3.233729077 × 1013
MonotonicityNot monotonic
2022-08-01T15:01:05.410682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4096158595
 
2.9%
24103576
 
1.9%
1291214
 
1.7%
883965
 
1.5%
154067798
 
1.2%
932851071
 
0.9%
51248479
 
0.9%
834047905
 
0.9%
5550447742
 
0.9%
18027447733
 
0.9%
Other values (310600)4733180
86.4%
ValueCountFrequency (%)
03514
 
0.1%
13542
 
0.1%
26989
 
0.1%
3739
 
< 0.1%
412793
 
0.2%
5289
 
< 0.1%
6164
 
< 0.1%
723
 
< 0.1%
883965
1.5%
925658
 
0.5%
ValueCountFrequency (%)
42782776841
< 0.1%
18862185421
< 0.1%
17777623681
< 0.1%
13544014761
< 0.1%
12081391161
< 0.1%
12000617802
< 0.1%
10902281921
< 0.1%
10737880001
< 0.1%
10737831681
< 0.1%
10737772241
< 0.1%

virtual_address
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct22515
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1124778.909
Minimum0
Maximum4290985216
Zeros158
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size41.8 MiB
2022-08-01T15:01:05.544884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4096
Q120480
median90112
Q3434176
95-th percentile3039232
Maximum4290985216
Range4290985216
Interquartile range (IQR)413696

Descriptive statistics

Standard deviation10255709.96
Coefficient of variation (CV)9.117978546
Kurtosis27911.85885
Mean1124778.909
Median Absolute Deviation (MAD)86016
Skewness99.64981013
Sum6.165203392 × 1012
Variance1.051795868 × 1014
MonotonicityNot monotonic
2022-08-01T15:01:05.676042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4096939479
 
17.1%
8192138740
 
2.5%
20480130909
 
2.4%
24576130215
 
2.4%
16384127554
 
2.3%
32768123668
 
2.3%
36864121599
 
2.2%
40960111030
 
2.0%
49152107138
 
2.0%
45056105251
 
1.9%
Other values (22505)3445675
62.9%
ValueCountFrequency (%)
0158
< 0.1%
121
 
< 0.1%
161
 
< 0.1%
371
 
< 0.1%
961
 
< 0.1%
1282
 
< 0.1%
1921
 
< 0.1%
3522
 
< 0.1%
39273
< 0.1%
4041
 
< 0.1%
ValueCountFrequency (%)
42909852161
< 0.1%
42909368321
< 0.1%
42908631041
< 0.1%
42885447681
< 0.1%
32212255361
< 0.1%
21487943681
< 0.1%
21474918401
< 0.1%
19370113111
< 0.1%
18096209921
< 0.1%
18090557441
< 0.1%

sec_name
Categorical

HIGH CARDINALITY

Distinct27944
Distinct (%)0.5%
Missing17172
Missing (%)0.3%
Memory size41.8 MiB
.rsrc
875638 
.text
795366 
.rdata
712092 
.data
702512 
.reloc
573612 
Other values (27939)
1804866 

Length

Max length8
Median length7
Mean length5.259908611
Min length1

Characters and Unicode

Total characters28740593
Distinct characters94
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18886 ?
Unique (%)0.3%

Sample

1st row.text
2nd row.data
3rd row.rdata
4th row.bss
5th row.idata

Common Values

ValueCountFrequency (%)
.rsrc875638
16.0%
.text795366
14.5%
.rdata712092
13.0%
.data702512
12.8%
.reloc573612
10.5%
.idata225117
 
4.1%
.pdata165744
 
3.0%
.tls156855
 
2.9%
.bss105612
 
1.9%
CODE80169
 
1.5%
Other values (27934)1071369
19.5%

Length

2022-08-01T15:01:05.803579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rsrc875666
16.0%
text796317
14.6%
data782434
14.3%
rdata727057
13.3%
reloc573622
10.5%
idata225650
 
4.1%
bss184312
 
3.4%
pdata165759
 
3.0%
tls156871
 
2.9%
code88298
 
1.6%
Other values (27234)888100
16.3%

Most occurring characters

ValueCountFrequency (%)
.4842611
16.8%
a3901380
13.6%
t3806636
13.2%
r3133317
10.9%
d2031305
7.1%
c1549885
 
5.4%
e1498985
 
5.2%
s1396796
 
4.9%
l883155
 
3.1%
x863147
 
3.0%
Other values (84)4833376
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21145215
73.6%
Other Punctuation4924266
 
17.1%
Uppercase Letter2155915
 
7.5%
Decimal Number472809
 
1.6%
Connector Punctuation32335
 
0.1%
Modifier Symbol4786
 
< 0.1%
Dash Punctuation2221
 
< 0.1%
Math Symbol1531
 
< 0.1%
Close Punctuation658
 
< 0.1%
Open Punctuation578
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a3901380
18.5%
t3806636
18.0%
r3133317
14.8%
d2031305
9.6%
c1549885
 
7.3%
e1498985
 
7.1%
s1396796
 
6.6%
l883155
 
4.2%
x863147
 
4.1%
o699648
 
3.3%
Other values (16)1380961
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
S252427
11.7%
P238625
11.1%
U221143
10.3%
A220694
10.2%
D203241
9.4%
X197981
9.2%
T136283
6.3%
E134207
 
6.2%
C115055
 
5.3%
B100396
 
4.7%
Other values (16)335863
15.6%
Other Punctuation
ValueCountFrequency (%)
.4842611
98.3%
/53144
 
1.1%
#15304
 
0.3%
\9279
 
0.2%
:850
 
< 0.1%
?509
 
< 0.1%
@490
 
< 0.1%
"381
 
< 0.1%
!368
 
< 0.1%
*242
 
< 0.1%
Other values (5)1088
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0185639
39.3%
1162032
34.3%
240165
 
8.5%
423274
 
4.9%
513227
 
2.8%
713148
 
2.8%
912397
 
2.6%
39287
 
2.0%
87736
 
1.6%
65904
 
1.2%
Math Symbol
ValueCountFrequency (%)
<319
20.8%
>305
19.9%
=288
18.8%
+274
17.9%
~173
11.3%
|172
11.2%
Close Punctuation
ValueCountFrequency (%)
]276
41.9%
)237
36.0%
}145
22.0%
Open Punctuation
ValueCountFrequency (%)
[229
39.6%
(212
36.7%
{137
23.7%
Modifier Symbol
ValueCountFrequency (%)
^4557
95.2%
`229
 
4.8%
Connector Punctuation
ValueCountFrequency (%)
_32335
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2221
100.0%
Currency Symbol
ValueCountFrequency (%)
$279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23301130
81.1%
Common5439463
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a3901380
16.7%
t3806636
16.3%
r3133317
13.4%
d2031305
8.7%
c1549885
 
6.7%
e1498985
 
6.4%
s1396796
 
6.0%
l883155
 
3.8%
x863147
 
3.7%
o699648
 
3.0%
Other values (42)3536876
15.2%
Common
ValueCountFrequency (%)
.4842611
89.0%
0185639
 
3.4%
1162032
 
3.0%
/53144
 
1.0%
240165
 
0.7%
_32335
 
0.6%
423274
 
0.4%
#15304
 
0.3%
513227
 
0.2%
713148
 
0.2%
Other values (32)58584
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII28740593
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4842611
16.8%
a3901380
13.6%
t3806636
13.2%
r3133317
10.9%
d2031305
7.1%
c1549885
 
5.4%
e1498985
 
5.2%
s1396796
 
4.9%
l883155
 
3.1%
x863147
 
3.0%
Other values (84)4833376
16.8%

Interactions

2022-08-01T15:00:40.147280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:17.575159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:22.003007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:26.562625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:31.405004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:35.703435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:40.889902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:18.351155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:22.735297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:27.380243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:32.121128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:36.445342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:41.644349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:19.125036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:23.507772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:28.209539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:32.855716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:37.206556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:42.380196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:19.862702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:24.250648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:29.025722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:33.557372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:37.947994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:43.122042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:20.581622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:24.986953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:29.844315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:34.264816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:38.666246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:43.841744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:21.284989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:25.724808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:30.668167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:34.975115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-01T15:00:39.410989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-01T15:01:05.916607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-01T15:01:06.040124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-01T15:01:06.162210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-01T15:01:06.284131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-01T15:00:46.164254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-01T15:00:50.602351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-01T15:00:56.713530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-01T15:00:59.344039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

filenamewin_countsha256imp_hashsec_chi2sec_entropysec_md5raw_sizevirtual_sizevirtual_addresssec_name
02022042215/2022042215_401b6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6faa9192bab5c7c795c7488b69a1853f9c2142740.565.21d04e1b35c9928ce904ea04494e9addd3563252844096.text
12022042215/2022042215_401b6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6faa9192bab5c7c795c7488b69a1853f9c2111591.000.56ee2380aae73db3186a5a54ce2f9ac7fc51212812288.data
22022042215/2022042215_401b6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6faa9192bab5c7c795c7488b69a1853f9c233541.004.583f6ce759ed044ca6fd40934ca8a1f973102475216384.rdata
32022042215/2022042215_401b6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6faa9192bab5c7c795c7488b69a1853f9c2-1.000.00d41d8cd98f00b204e9800998ecf8427e049620480.bss
42022042215/2022042215_401b6c14d06dbcd3da476d84a872f1e821623af9b03e97c862884676345d8fca6faa9192bab5c7c795c7488b69a1853f9c2178529.003.24cc77684f8c03f3a87d249f96bcd88fd52048158824576.idata
52022042215/2022042215_402d0a07782408df389b266d5da214399f228605da8a2e0098a07f3869421ef0255a9192bab5c7c795c7488b69a1853f9c2142740.565.21d04e1b35c9928ce904ea04494e9addd3563252844096.text
62022042215/2022042215_402d0a07782408df389b266d5da214399f228605da8a2e0098a07f3869421ef0255a9192bab5c7c795c7488b69a1853f9c2111591.000.56ee2380aae73db3186a5a54ce2f9ac7fc51212812288.data
72022042215/2022042215_402d0a07782408df389b266d5da214399f228605da8a2e0098a07f3869421ef0255a9192bab5c7c795c7488b69a1853f9c233541.004.583f6ce759ed044ca6fd40934ca8a1f973102475216384.rdata
82022042215/2022042215_402d0a07782408df389b266d5da214399f228605da8a2e0098a07f3869421ef0255a9192bab5c7c795c7488b69a1853f9c2-1.000.00d41d8cd98f00b204e9800998ecf8427e049620480.bss
92022042215/2022042215_402d0a07782408df389b266d5da214399f228605da8a2e0098a07f3869421ef0255a9192bab5c7c795c7488b69a1853f9c2178529.003.24cc77684f8c03f3a87d249f96bcd88fd52048158824576.idata

Last rows

filenamewin_countsha256imp_hashsec_chi2sec_entropysec_md5raw_sizevirtual_sizevirtual_addresssec_name
54812482022042404/2022042404_41374944f2d856bba345721c3bff31e30f476e08f0af56dd209953e59d7abf5c54bc68168abecba2211e61763c4c9ffcaa13369e511167.002.7717ba5940940f6b003c9ab874b48d12d44096196212288.rdata
54812492022042404/2022042404_41374944f2d856bba345721c3bff31e30f476e08f0af56dd209953e59d7abf5c54bc68168abecba2211e61763c4c9ffcaa13369e931117.880.64af19baff048a7ca28bd9b67dd9c2fd1c409635216384.data
54812502022042404/2022042404_41374944f2d856bba345721c3bff31e30f476e08f0af56dd209953e59d7abf5c54bc68168abecba2211e61763c4c9ffcaa13369e140906.755.186f84bbdf1470be6e99afb26212ea9e4b4096342420480.rsrc
54812512022042404/2022042404_413749454eedbe319f6041002d9ae9420363b91d0aa264ee1fdf2b380e9fccc196d7ed7f4c4ecd9ff868f9418a5eb6affb43c30c1331833.506.34214301d1932dc4971ab42bd6a3acde072012162008594096.text
54812522022042404/2022042404_413749454eedbe319f6041002d9ae9420363b91d0aa264ee1fdf2b380e9fccc196d7ed7f4c4ecd9ff868f9418a5eb6affb43c30c2679567.255.0867bf2ec9c0cac531f1a7ceb6f7c7515c6092860508208896.rdata
54812532022042404/2022042404_413749454eedbe319f6041002d9ae9420363b91d0aa264ee1fdf2b380e9fccc196d7ed7f4c4ecd9ff868f9418a5eb6affb43c30c129229.844.9940c51dc6c5dad254473ecd4c9d9de2aa1075213296270336.data
54812542022042404/2022042404_413749454eedbe319f6041002d9ae9420363b91d0aa264ee1fdf2b380e9fccc196d7ed7f4c4ecd9ff868f9418a5eb6affb43c30c386016.035.459f6c7192b8e73df286eda066a6c8b5381280012492286720.pdata
54812552022042404/2022042404_413749454eedbe319f6041002d9ae9420363b91d0aa264ee1fdf2b380e9fccc196d7ed7f4c4ecd9ff868f9418a5eb6affb43c30c125503.000.1872d010ca770aa27d9a30ecfded41306f51224303104.didat
54812562022042404/2022042404_413749454eedbe319f6041002d9ae9420363b91d0aa264ee1fdf2b380e9fccc196d7ed7f4c4ecd9ff868f9418a5eb6affb43c30c3150183.504.24c40f2f34b58267ba2b002139d39800ef6502464720307200.rsrc
54812572022042404/2022042404_413749454eedbe319f6041002d9ae9420363b91d0aa264ee1fdf2b380e9fccc196d7ed7f4c4ecd9ff868f9418a5eb6affb43c30c59981.384.2394b4856487e239b6a1218d7a4c61924615361044372736.reloc

Duplicate rows

Most frequently occurring

filenamewin_countsha256imp_hashsec_chi2sec_entropysec_md5raw_sizevirtual_sizevirtual_addresssec_name# duplicates
02022042404/2022042404_293683018e37c4a6697b836c5be511e3aea64142a5de92d7a58cbc6b4fd0f39fff65ed4b23b7a2ad6dd5722f5566eaa0d8a348bf16891974.03.45c2ec5777bf08015cf7e7efb195a8ed55145408145408643072.rsrc2